Google Gemini

Google Gemini API pricing calculator

Use one workload baseline to estimate monthly spend across every tracked Google Gemini model — input, cached-input, and output tokens included. Rank Google Gemini against 10 other providers on the same traffic pattern.

Related: compare/openai vs anthropic · topics/cheapest llm api · methodology

Calculator

Step 1

Describe your workload

Start with a preset or dial in your own numbers.

Synced Jun 29, 2026

1K messages / mo

1K tokens avg

Step 2

Estimated monthly spend

$3.06

Rank #43 of 63 · save $3.00/mo vs #1

Input $0.56 Cached 0.34M tok Output $2.50
Total tokens1.00M
Messages1K
Avg tok/msg1K

Balanced quality/price workloads and multimodal app features.

Step 3

Compare all models

9 models priced for your workload

Best value

02
Gemini 2.0 Flash-Lite (batch)Google Gemini
Estimated$0.07
04
Gemini 2.5 Flash-Lite (batch)Google Gemini
Verified$0.07
05
Gemini 2.0 Flash (batch)Google Gemini
Estimated$0.08
09
Gemini 2.0 Flash (standard)Google Gemini
Estimated$0.11
11
Gemini 2.0 Flash-Lite (standard)Google Gemini
Verified$0.13
12
Gemini 2.5 Flash-Lite (standard)Google Gemini
Verified$0.14
26
Gemini 2.5 Flash (standard)Google Gemini
Verified$0.76
34
Gemini 2.5 Pro (batch)Google Gemini
Verified$1.55
43
Gemini 2.5 Pro (standard)Google Gemini
Verified$3.06

How costs are calculated

Prices from ai-provider-pricing-validated.json, validated Jun 2026. Confirm on official provider pages before billing decisions.

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Model Cost Comparison · Built by Lazige · Methodology

How we calculate cost

Monthly estimate = (input tokens × input $/MTok) + (cached tokens × cached $/MTok when published) + (output tokens × output $/MTok), scaled to your message volume. See the methodology for validation sources and update cadence.

Use cases

Common workload patterns teams model here

Illustrative scenarios — not customer testimonials. Each card shows how a typical team shape (support bot, RAG, code assistant, or agent) maps to the calculator presets.

“A support-bot preset with 50k messages/month surfaced three budget models in one pass — faster than copying rates from five pricing pages.”
B2B SaaS support botHigh volume · short replies · 55% cache
“Raising the cached-input slider made our RAG estimate realistic. We moved retrieval-heavy traffic to a cheaper model without changing reply quality.”
Document Q&A / RAGRetrieval-heavy · 65% cache
“PMs use the embed on internal docs to sanity-check model spend before vendor requests — everyone shares the same workload baseline.”
Platform / internal toolingMixed presets · stakeholder decks
“Before scaling an agent workflow, comparing monthly cost across every provider for the same token mix avoided over-provisioning on day one.”
Tool-calling agentAgent preset · multi-step I/O
“Finance teams grasp token mix faster with one screenshot from the ranking table — useful when justifying a move off a default premium model.”
Cost review / FinOpsBoard prep · usage doubles scenario
“Quarterly Bedrock vs Vertex vs direct API reviews start here — normalize the math before opening vendor spreadsheets.”
Cloud architecture reviewMulti-cloud comparison
“The code-assistant preset was a realistic starting point for a copilot MVP; we adjusted tokens after a pilot week and stayed within 10% of the estimate.”
IDE / code copilotCode preset · long context
“Gemini Flash placed top three for our exact cache ratio on a high-volume FAQ bot — easy to miss in a static pricing table.”
High-volume FAQSupport preset · high cache